This page is a collection of my blog posts, talks, publications, ideas and projects. They touch on machine learning and computer science, neuroscience and brain-computer interfaces, reinforcement learning and robotics.

As a machine learning architect at OffWorld Inc I am leading a machine learning team to train robots for industrial applications in unstructured environments, aiming at space exploration. In my opinion, reinforcement learning has reached a point where we can attempt to deploy it on real-world robots for practical applications.

In parallel I am working on my PhD in neuroscience and artificial intelligence at University of Tartu. My focus is on interpreting machine learning models that were trained to decode intracerebral electrophysiological activity of human brain. For a long time humans were in the business of carefully crafting elegant models that describe the data. An accurate and insightful model reveals internal dynamics of a system and leads to knowledge. In the modern times the amounts of data to sift thorugh became unmanageable, so we have invented machine learning algorithms to look for specific things in those volumes of data. However, something was lost in that transition from manual to automated modeling. And that something is the precise understanding of how exactly a model operates. I believe that interpretation of machine learned models, in particular the ones that were trained on brain data, is the way to get the best of the both words — an automated model-building capability that can process huge volumes of data and careful and insightful understanding of the underlying process.